Detection of information concealment and mitigation via introduction of deliberate variance

A computing system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media that store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
• obtaining network information for a plurality of packets transmitted by a transmitting entity, wherein the network information is descriptive of temporal features of network events that occurred during transmission of the plurality of packets;
• extracting one or more feature tuples for one or more packets of the plurality of packets, wherein each of the one or more feature tuples is indicative of the temporal features of the network events that occurred for the plurality of packets;
• processing the one or more feature tuples with a machine-learned hidden information detection model to obtain a prediction output that identifies one or more modulated features from features included in the one or more feature tuples, and wherein the one or more modulated features are modulated to conceal hidden information;
• obtaining information indicative of a second plurality of packets for transmission by the transmitting entity;
• and
• based on the prediction output, causing a variance addition process to be performed for the second plurality of packets, wherein the variance addition process causes deliberate variance to be added to the temporal features of future network events for at least some of the second plurality of packets.

One or more non-transitory computer-readable media that store instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising:

• obtaining network information for a plurality of discrete information units transmitted by a transmitting entity, wherein the network information is descriptive of temporal features of network events that occurred for the plurality of discrete information units;
• processing the network information with a machine-learned hidden information detection model to obtain a prediction output indicating that the temporal features of the network events that occurred for the plurality of discrete information units are modulated to conceal hidden information;
• and
• based on the prediction output, causing a variance addition process to be performed for the transmitting entity, wherein the variance addition process causes deliberate variance to be added to temporal features of future network events for future transmissions of discrete information units by the transmitting entity.